Bayesian BIM-Guided Construction Robot Navigation with NLP Safety Prompts in Dynamic Environments
Mani Amani, Reza Akhavian
TL;DR
This work tackles safe and adaptive robot navigation on dynamic construction sites by fusing BIM-derived context with NLP-driven sentiment to steer probabilistic path planning. It introduces a Bayesian framework that updates an Exponential Potential Field based on prompts, combined with Multi-heuristic A* to balance safety and efficiency. The approach demonstrates that sentiment-driven prompts can meaningfully alter paths, improving obstacle clearance under safety emphasis while maintaining reasonable route lengths. By integrating semantic BIM information, real-time prompts, and Bayesian fusion, the method offers a flexible, human-in-the-loop foundation for safer construction robot navigation in changing environments.
Abstract
Construction robotics increasingly relies on natural language processing for task execution, creating a need for robust methods to interpret commands in complex, dynamic environments. While existing research primarily focuses on what tasks robots should perform, less attention has been paid to how these tasks should be executed safely and efficiently. This paper presents a novel probabilistic framework that uses sentiment analysis from natural language commands to dynamically adjust robot navigation policies in construction environments. The framework leverages Building Information Modeling (BIM) data and natural language prompts to create adaptive navigation strategies that account for varying levels of environmental risk and uncertainty. We introduce an object-aware path planning approach that combines exponential potential fields with a grid-based representation of the environment, where the potential fields are dynamically adjusted based on the semantic analysis of user prompts. The framework employs Bayesian inference to consolidate multiple information sources: the static data from BIM, the semantic content of natural language commands, and the implied safety constraints from user prompts. We demonstrate our approach through experiments comparing three scenarios: baseline shortest-path planning, safety-oriented navigation, and risk-aware routing. Results show that our method successfully adapts path planning based on natural language sentiment, achieving a 50\% improvement in minimum distance to obstacles when safety is prioritized, while maintaining reasonable path lengths. Scenarios with contrasting prompts, such as "dangerous" and "safe", demonstrate the framework's ability to modify paths. This approach provides a flexible foundation for integrating human knowledge and safety considerations into construction robot navigation.